RESEARCH

Research

Wireless at Rice ECE started in 1985 with an emphasis on physical layer algorithms and architectures for wireless. Since then, the group has continued to grow rapidly in new directions, including network protocols, experimental platforms, mobile systems, high-speed circuits, mmWave and THz networking, and most recent machine learning for wireless. Some of notable milestones include

The group now includes seven Rice faculty, and many national and international collaborators.

  • ASTRO: The ASTRO project advances technology to build a platform for autonomous data-driven mobile sensing via networked drones. We target proof-of-concept demonstrations for a diverse set of unprecedented sensing capabilities including (i) high-resolution distributed mobile laser-spectroscopy gas sensing to identify, localize, and track health and environmental hazards in real-time and (ii) automated mobile radio-frequency spectrum analysis and usage via distributed diverse-spectrum virtual arrays. The main objective of ASTRO is to realize high-resolution pollution sensing by exploiting the ability of our drones platform to dynamically move sensors in 3-D according to real-time measurements. We mainly target the detection, tracking, and modeling of high VOC concentration levels following extreme events.
  • Terahertz (THz) networking: We are developing foundations for terahertz networking, spanning antenna and propagation fundamentals, high-speed circuits, intelligent antennas and networking fundamentals to match the special characteristics of wireless communications in THz frequencies.
  • RENEW Wireless – an NSF PAWR Platform: The RENEW project will develop world’s first fully programmable and observable wireless radio network. With RENEW, wireless research and development community will be able to test diverse ideas and concepts, ranging from low-level hardware to all the way to novel applications. The project will support many firsts, like
    • Programmable wide-band radios, including 5G bands of 2.5 and 3.5 GHz
    • Large-scale MIMO, including massive MIMO
    • Novel PHY and network stacks, including 5G-like and WiFi-like protocol stacks
    • Observable at all layers of the protocol stack
    • Use of distributed computational resources throughout the infrastructure
  • Real-time machine learning: Recent breakthroughs in deep neural networks (DNNs) have fueled a growing demand for intelligent edge devices featuring real-time and on-site learning. However, the practical realization of such systems remains a challenge due to the limited computing and energy resources available at the edge, as well as the massive and growing learning costs for state-of-the-art DNNs. The overarching goal for the research is to foster a systematic breakthrough in real-time machine learning (RTML) by harmonizing algorithm-, architecture-, and circuit-level innovations. The PIs will demonstrate these innovations using drone-based, on-board real-time object detection from video.
  • Autonomous networking: A new exciting research direction is how to make large-scale networks, like city-scale cellular networks, enterprise-scale WiFi deployments or distributed networks completely autonomous, which can operate at optimal points of performance and security. The new problems are at the intersection of wireless, security and (graph) neural networks.